Building AI Agents with LangGraph: From Fundamentals to Production
Building AI Agents with LangGraph: From Fundamentals to Production A comprehensive guide to building reliable, stateful AI agents using LangGraph —…
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How We Stopped AI Agents from Hallucinating Tool Schemas & Wasting API Credits
If you’re building autonomous AI agents using frameworks like LangChain, CrewAI, AutoGen, or raw function calling, you’ve likely encountered this sc…
How to Evaluate Voice Agents with LangSmith | Focused
A voice agent finishing a call doesn't mean it worked. A scheduling agent can call the right tools in the right order and still book the wrong time,…
Keep Every LangSmith Trace Without the 10 Retention Bill
LangSmith is excellent for debugging live AI systems. But keeping every trace in its extended-retention tier can turn observability into a surprisin…
Deep Agents: open source-обвязка на LangGraph
Привет, на связи команда GigaChain! Мы занимаемся агентными системами и развиваем набор open source-решений для подключения агентов к GigaChat API.…
Graph Engineering Explained: The Missing Fifth Layer of AI Agent Architecture
Every "my agent isn't working" postmortem starts the same way: someone rewrites the prompt. Adds a constraint. Adds an example. Ships it again. Thre…
Benchmarking Gemini, Claude, and OpenAI for Structured Document Extraction
A few fellow engineers and I were talking about field extraction the other day. They were both saying Gemini's been surprisingly good at pulling str…
LCEL vs. Legacy Chains: From Recipe Confusion to Clear Pipelines 🚀
TL;DR: Legacy Chains are the old way—rigid, class-heavy, and frustrating. LCEL is the new standard—visual, modular, and future-proof. Always choose…
Human Oversight of AI Agents Failed 33% of the Time in Testing
When AI agents ask for permission to act, how often do humans actually catch the dangerous ones? A study on AI agent command approval accuracy acros…
Top 5 Dev Tools Released in Late July 2026
LangChain 0.3.0 LangChain 0.3.0 is a popular AI framework that enables developers to build applications with large language models through enhanced…
Handoffs can turn one task into a 15x token bill
Handoffs are useful when a specialist agent needs to take over a task. They also make cost easier to hide, because the bill is spread across graph n…
Stop your Grok 4.5 agents from blowing past the context window
Long-running agents have a boring failure mode: they accumulate conversation until they hit the model's context window and fall over. xAI ships a fi…
Understanding Middleware in Deep Agents (With Runnable Examples)
If you've built even a simple AI agent, you've probably noticed that the "agent loop" itself is deceptively simple: the model gets a message, decide…
LangChain Advanced Patterns: Building Production-Grade AI Systems
Beyond Basic LangChain You've built a simple agent. Now scale it. Production LangChain systems require: Memory management Error handling Performance…
LLM-wiki против RAG: Оцениваем и сравниваем
Про LLM-wiki здесь уже было несколько хороших статей ( 1 , 2 и 3 ), поэтому подробно останавливаться на идее Andrej Karpathy не буду. В двух словах:…
Building a Robust RAG Pipeline Architecture for Production
Answer up front: A RAG pipeline architecture is a set of connected services that ingest raw documents, turn them into embeddings, store them in a ve…
Why Prompt Engineering Isn't Enough for Production AI Agents
TL;DR: Autonomous Agents frequently get trapped in execution loops, burning through API tokens and compute. Prompt engineering can't guarantee execu…
Building a Cross-Border Price-Comparison Agent: A Live Build Log
Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show wh…
Orchestrating AI: LangChain Framework Abstraction vs. Pure Native Code
When building prototypes with Generative AI, velocity is everything. Developers want to stitch together prompts, text splitters, vector stores, and…
Building AI agents with LangChain
LangChain agents are built on LangGraph : the model calls tools in a loop until it returns a final answer. The high-level entry point is createAgent…
Agent Series (20): Harness in Production — From Single File to Reusable Package
From Demo Code to a Reusable Package Article 19 used a 900-line harness_full_demo.py to demonstrate eight defense layers. That file is good for expl…
LangChain + Chroma: Multi-turn RAG Memory and Automated Testing That Turned 2-Hour Bugs Into 5-Minute Fixes
At 1 a.m., the customer group chat exploded: “Does your customer service bot have only a 7-second memory? I just gave it the order number, and the n…
How to Build an AI Resume Builder with LangChain and Node.js
A few months back, my friend Marcus was applying for a senior backend role at a fintech company. He had five years of solid experience — distributed…
Agent Series (10): MCP Protocol — Standardizing the Tool Ecosystem
More Tools, More Chaos After you build an Agent, the first thing you usually do is give it tools: search, code execution, database queries, API call…
How to Add Memory Security to Your LangChain Agent in 5 Minutes
Why Your Agent's Memory Needs Security If you're building LangChain agents with persistent memory (ConversationBufferMemory, RedisChatMessageHistory…
Agent Series (3): Plan-and-Solve — Think First, Then Act
Where Does ReAct Hit a Wall? The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the ne…
Three Design Decisions That Shaped the Enterprise RAG Retrieval Pipeline
Enterprise RAG — A practitioner's build log | Post 3 of 6 A retrieval pipeline has more design surface than it appears. The technology choices — vec…
AgentKit vs LangChain vs Direct HTTP — picking the right integration for paid agent APIs
When you're plugging an LLM agent into an external API, you have three reasonable patterns: hand-rolled HTTP, AgentKit's action provider model, or L…
LangChain ChromaDB Metadata Priority Injection — RAG Poisoning Vulnerability
LangChain ChromaDB Metadata Priority Injection Vulnerability Summary LangChain's Chroma integration allows attackers to manipulate document retrieva…
AI Agent Frameworks Compared: LangChain vs Custom vs Agentic Systems
AI agents are rapidly moving from experimental prototypes to production infrastructure. Developers are no longer just building chatbots. They’re bui…